Electronic device and related methods for predicting initiation of establishment of a network with one or more other electronic devices

ABSTRACT

An electronic device includes a memory circuitry, an interface circuitry, a sensor circuitry, and a processor circuitry having a predictor circuitry configured to operate according to a prediction model. The processor circuitry is configured to obtain sensor data. The processor circuitry is configured to determine, based on the sensor data, using the predictor circuitry, a predicted time parameter indicative of a prediction of a time slot to initiate an establishment of a network with one or more other electronic devices. The processor circuitry is configured to initiate the establishment of the network with the one or more other electronic devices according to the predicted time parameter.

RELATED APPLICATION DATA

This application claims the benefit of Swedish Patent Application No.2050336-3, filed Mar. 26, 2020, the disclosure of which is incorporatedherein by reference in its entirety.

TECHNICAL FIELD

The present disclosure pertains to the field of Internet of things, andelectronic devices. The present disclosure relates to an electronicdevice and to a method for predicting initiation of establishment of anetwork with one or more other electronic devices.

BACKGROUND

There are many scenarios where electronic devices (such as Internet ofthings, IoT, devices and sensor devices) need to perform some sensingtasks and report measured values (such as to a server). Local networkssuch as mesh networks may be used to communicate between electronicdevices (for example, between low power consumption electronic devices,such as short range bearers). A mesh network topology may provide thepossibility to communicate efficiently between devices with low powerconsumption. The mesh protocol may handle routing and make the networkself-managing and self-healing. The routing of package to the correctdestination may be handled by the network protocol which hides thecomplexity of reaching correct end node from the application layer.

However, local networks such as mesh networks may provide a more complexcommunication with more steps (such as more signaling). For example,when electronic devices join or leave the network or move physically,the network topology is changed.

Those changes may need to be communicated to other nodes in the networkto make correct routing decisions (such as propagated to all routers,such that the routing tables may be updated in each node acting as arouter). Therefore, current mesh protocols experience high powerconsumption when the electronic devices (such as sensor devices) aremoving or leaving or joining the network (such as often, such as with ahigh frequency). For example, electronic devices that are joining orleaving the network may cause an increased load in the network.

Power optimization is therefore a challenging aspect of the design ofelectronic devices such as connected devices and IoT devices. It may bechallenging to reduce energy consumption of electronic devices forexample to ensure that a battery powered electronic device may beworking for its intended operation time (such as battery powered devicesthat should be able to run for months without recharging or replacingthe battery).

SUMMARY

Existing networks may comprise mechanisms for improving power efficiency(such as by introducing time slotting) when the network has a stabletopology. For example, even more complex nodes in the networks, likerouting nodes, may be in low power mode when there is no data trafficscheduled. This may work well for networks where the nodes are notmoving or leaving or joining the network (such as electronic devicesjoining and/or leaving the network with a low frequency).

However, this needs to be improved for electronic devices in a networkthat experiences frequent topological changes (for example with mobilenodes). For example, when an electronic device (for example, so-callednode) in the network moves, the electronic device needs to detect newneighbor electronic device (such as new neighbor nodes), to maintain theconnection to the network. In addition, the changes may need to bepropagated to other routers in the network so that the routing tablesmay be updated in the routing nodes. This overhead consumes power andalso affects the robustness of the network since it takes time beforethe routing tables are updated.

In many use cases, it may not be required to have the network up andrunning at all time (such as 24/7). For example, tasks like harvestingof sensor data or distribution of content are not required to be done ina real time fashion, and may be allowed to be done over hours or days.There is a need for a solution where a network may be established onlywhen it is convenient (such as only when it is predicted that it is agood time to do so).

Accordingly, there is a need for electronic devices and methods forpredicting initiation of establishment of a network with one or moreother electronic devices which mitigate, alleviate or address theshortcomings existing and provide an improved establishment of networkwith improved timing of establishment of a network and an improved powerefficiency of electronic devices.

The present disclosure provides an electronic device. The electronicdevice comprises a memory circuitry, an interface circuitry, a sensorcircuitry, and a processor circuitry. The processor circuitry comprisesa predictor circuitry configured to operate according to a predictionmodel. The processor circuitry is configured to obtain sensor data. Theprocessor circuitry is configured to determine based on the sensor data,using the predictor circuitry, a predicted time parameter indicative ofa prediction of a time slot to initiate an establishment of a networkwith one or more other electronic devices. The processor circuitry isconfigured to initiate the establishment of the network with the one ormore other electronic devices according to the predicted time parameter.

Further, a method, performed by an electronic device, for predictinginitiation of establishment of a network with one or more otherelectronic devices, is provided. The method comprises obtaining thesensor data. The method comprises determining, based on the sensor dataand a prediction model, a predicted time parameter indicative of aprediction of a time slot to initiate an establishment of a network withone or more other electronic devices.

The method comprises initiating the establishment of the network withthe one or more other electronic devices according to the predicted timeparameter.

It is an advantage of the present disclosure that the networkestablishment is improved, allowing the electronic device disclosedherein to establish a network with other electronic device(s) at asuitable time based on prediction. For example, by using a predictorcircuitry (such as to provide a prediction of a time slot to initiate anestablishment of a network with one or more other electronic devices)the network may for example be established when the electronic device(s)forming the network are likely to have a static location for a period oftime. By using a predictor circuitry, it may be possible to do theprediction based on sensor data indicative of a context of theelectronic device (such as activity).

It is an advantage of the present disclosure that the power consumptionof the electronic device may be more efficient and thereby reduced. Thismay be achieved by optimizing the initiation of the establishment of thenetwork with the one or more other electronic devices according to thepredicted time parameter, such as to reduce unnecessary communicationand return to low power mode once the scheduled tasks have beenconcluded over the network. In addition, by continuously learning andsharing parameters of the predictor circuitry with other nodes in thenetwork, the predictor circuitry is capable of adapting and improvingthe predictions over time, based on the environment. By sharingpredictor parameters among nodes in the network, the predictor circuitrymay be improved: faster processing and lower communication overhead.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other features and advantages of the present disclosurewill become readily apparent to those skilled in the art by thefollowing detailed description of example embodiments thereof withreference to the attached drawings, in which:

FIG. 1 is a schematic representation illustrating an example of ascenario for predicting initiation of establishment of a network withone or more other electronic devices according to one or moreembodiments of this disclosure,

FIG. 2 is a block diagram illustrating an example electronic deviceaccording to this disclosure, and

FIGS. 3A and 3B are flow-charts illustrating an example method,performed by an electronic device, for predicting initiation ofestablishment of a network with one or more other electronic devicesaccording to this disclosure.

DETAILED DESCRIPTION

Various example embodiments and details are described hereinafter, withreference to the figures when relevant. It should be noted that thefigures may or may not be drawn to scale and that elements of similarstructures or functions are represented by like reference numeralsthroughout the figures. It should also be noted that the figures areonly intended to facilitate the description of the embodiments. They arenot intended as an exhaustive description of the disclosure or as alimitation on the scope of the disclosure. In addition, an illustratedembodiment needs not have all the aspects or advantages shown. An aspector an advantage described in conjunction with a particular embodiment isnot necessarily limited to that embodiment and can be practiced in anyother embodiments even if not so illustrated, or if not so explicitlydescribed.

The figures are schematic and simplified for clarity, and they merelyshow details which aid understanding the disclosure, while other detailshave been left out. Throughout, the same reference numerals are used foridentical or corresponding parts.

FIG. 1 is a schematic representation 500 illustrating an example of ascenario for predicting initiation of establishment of a network withone or more other electronic devices according to one or moreembodiments of this disclosure. FIG. 1 shows an electronic device 300and one or more other electronic devices 300A. Optionally, a servicedevice 400 (such as having a role of a border gateway) may be present,for example to report the sensor data to an external network (such asthe internet, a server, and/or a cloud server). The border gateway maybe an internet protocol (IP) router, such as a gateway to the internet.The border gateway may comprise a first interface (such as a firstnetwork interface) to the network including the electronic devices 300,300A and a second interface (such as a second network interface, forexample via connections such as long term evolution, LTE, wirelessfidelity, Wi-Fi, and/or an ethernet cable) to an external network (suchas a global network, such as the internet).

In some embodiments, the service device 400 may be comprised in theelectronic device 300 (such as co-located with the electronic device).

The electronic device 300 and the one or more other electronic devices300A may be seen as wireless connectivity devices such as an IoT devices(such as sensor devices, for example nodes comprising a short rangeradio circuitry, a clock circuitry, and a predictor circuitry)configured to obtain sensor data, to determine based on the sensor data,using the predictor circuitry, a predicted time parameter indicative ofa prediction of a time slot to initiate an establishment of a networkwith one or more other electronic devices, and to initiate theestablishment of the network with the one or more other electronicdevices according to the predicted time parameter.

The example electronic device 300 illustrated in FIG. 1 shows a fourstep approach of a scenario for predicting initiation of establishmentof a network with one or more other electronic devices. In a first step502, the electronic device 300 and the one or more other electronicdevices 300A obtain sensor data, for example the electronic device 300and the one or more other electronic devices 300A may be considered as agroup of sensing devices in low power mode obtaining sensor data (suchas data sensed by the sensor circuitry). In a second step 504, theprocessor circuitry (such as processing circuitry 302 of FIG. 2 ) isconfigured to determine, based on the sensor data, using the predictorcircuitry, a predicted time parameter indicative of a prediction of atime slot to initiate an establishment of a network with one or moreother electronic devices (such as the predictor indicates that a networkshall be formed). The sensor data used by the predictor circuitry andthe data sensed by the sensor circuitry of the electronic device 300 maybe different sensor data. For example, in a scenario where theelectronic device is used for goods in a refrigerated truck, the enduser may only be interested in sensor data such as temperature datasensed by a sensor circuitry such as a thermometer, whereas for thepredictor circuitry, sensor data such as motion data from a sensorcircuitry such as an accelerometer may be more relevant. The processorcircuitry is configured to initiate the establishment of the networkwith the one or more other electronic devices 300A according to thepredicted time parameter. In other words, the electronic device 300 andthe one or more other electronic devices 300A can start to form thenetwork based on the determination of the predictor circuitries in theelectronic devices. In a third step 506, the initiation is completed,and the network is established (in other words the network is formed)and a task may be executed (for example the sensor data may be reported,such as upload the sensed data). In a fourth step 508, the task has beenperformed or completed (for example the sensor data has been reported)and the electronic device 300 and the one or more other electronicdevices 300A may enter a power saving mode.

Example environments where the disclosed technique may be applied mayinclude applications such as farming (for example, a herd of animalswith sensor devices, such as activity sensors, for example for use insmart farming), tracking (such as trackers loaded on vehicles, forexample trucks, ferries, trains, cars in a parking lot and/or plains,and/or trackers in a warehouse, such as trackers loaded on packages),sensing of humans (such as sitting at a desk or on the same train)). Forexample, a condition of an animal may be detected and analysed (forexample if the activity of an animal is lower, it may be sick, or if thetemperature of the animal is higher, the animal may be in gestation).Further, it may be possible to have a traceability of an object that theelectronic device is mounted on (for example if a herd of animals aregrazing in different areas, it may be possible to track which area isthe best for the meat outcome, for example when a good is tracked tohave enter a zone, a notification may be sent to recipient or sender).The present disclosure may be applicable to star networks as well, whererouting information needs to be transferred to master nodes and theback-bone network for the master network.

The present disclosure proposes techniques for how to determine apredicted time parameter indicative of a prediction of a time slot toinitiate an establishment of a network, for example to predict aconvenient time for establishing the network and eventually execute atask (such as reporting sensor data). In other words, the presentdisclosure proposes techniques where electronic devices have thecapability to establish (such as form) a local network over theelectronic devices (such as short range bearers), where the establishingof the local network and execution of a task (such as reporting sensordata) in the network may be triggered based on a predicted timeparameter indicative of a prediction of a time slot (by using apredictor circuitry, such as a context aware predictor).

FIG. 2 is a block diagram illustrating an example electronic device 300according to one or more embodiments of the present disclosure.

The electronic device 300 comprises a memory circuitry 301, an interfacecircuitry 303, a sensor circuitry 304, and a processor circuitry 302comprising a predictor circuitry 302A. The predictor circuitry 302A isconfigured to operate according to a prediction model. The electronicdevice 300 may for example be a portable electronic device, such as anIoT device, a sensing device, an activity sensor, and/or a trackingdevice. The predictor circuitry 302A may be configured to operateaccording to one or more prediction models of various complexity. In oneor more embodiments, the predictor circuitry 302A may be configured tooperate according to a prediction model that may be configured manually(such as a minimal predictor). This may for example be a low efficiencyscheduler that guarantees a minimum quality of service.

The processor circuitry 302 is configured to obtain sensor data. In oneor more embodiments, the sensor data may be obtained by the processorcircuitry 302 from the sensor circuitry 304. The sensor data maycomprise one or more of: location data (such as global positioningsystem (GPS) coordinates) from a sensor circuitry such as positioningsensor, accelerometer data from a sensor circuitry such as anaccelerometer, time data (such as time of the day) from a clock,gyroscope data, temperature data from a thermometer, sleep data (such asmeasured or sensed by a sleep sensor), pulse data (such as measured orsensed by a pulse sensor), light data from a photo-sensor, humidity datafrom a hygrometer, sound data (such as voice) data from a microphone,photo or video data from a camera, and/or pressure data from a pressuresensor.

The processor circuitry 302 is configured to determine, based on thesensor data, using the predictor circuitry 302A, a predicted timeparameter indicative of a prediction of a time slot to initiate anestablishment of a network with one or more other electronic devices.

A predicted time parameter indicative of a prediction of a time slot toinitiate an establishment of a network with one or more other electronicdevices may in other words comprise or be indicative of a time where itis likely to successfully establish a network.

The predicted time parameter may be indicative of a prediction of a timeslot where it is suitable, opportune, and/or convenient for theelectronic device to establish the network with one or more otherelectronic devices. This may for example be a time slot where theelectronic device 300 and the one or more other electronic devices 300Aare within a certain distance from each other (for example within aradius of 1 m, 2 m, 5 m, 10 m, 30 m, 50 m, 100 m).

In other words, the processor circuitry 302 is configured to generate byfeeding the sensor data (such as sensor values) for example in a recenttime window to the predictor circuitry 302A, whereby a predicted timeparameter indicative of a prediction of a time slot to initiate anestablishment of a network with one or more other electronic devices(such as a trigger that may be generated for when it is feasible toestablish the network).

The predicted time parameter may comprise or be indicative of one ormore time periods, time slots, and/or time intervals. In one or moreembodiments, the sensor data may be used to predict a time suitable toestablish the network.

The processor circuitry 302 is configured to initiate the establishmentof the network with the one or more other electronic devices accordingto the predicted time parameter. The processor circuitry 302 may beconfigured to successfully establish a network. In one or moreembodiments, the processor circuitry 302 may be configured to report theobtained sensor data. The network may be a decentralized type ofwireless network comprising a plurality of electronic devices. Thenetwork to be established may for example comprise a local network,and/or an ad hoc network, such as a mobile ad hoc network, MANET, a meshnetwork, a star network and/or a tree network.

In one or more embodiments, the processor circuitry 302 may beconfigured to determine that the one or more other electronic devicesstart joining the established network when each of the one or moreelectronic device have determined (for example by using their ownpredictor circuitries) a predicted time parameter to establish thenetwork (for example, a feasible time that is likely to overlap orcoincide with the predicted time parameter determined by electronicdevice 300). In one or more embodiments, the one or more otherelectronic devices may comprise equivalent predictor circuitries. Forexample, the one or more other electronic devices may be peers of theelectronic device 300.

The processor circuitry 302 may be configured to initiate theestablishment of the network with the one or more other electronicdevices according to the predicted time parameter and aQuality-of-Service (QoS) mechanism, in order to guarantee a minimum QoS.In one or more embodiments, the processor circuitry may be configured toinitiate the establishment of the network with the one or more otherelectronic devices according to the predicted time parameter such that aminimum quality of service may be provided.

The processor circuitry 302 may be configured to initiate theestablishment of the network with the one or more other electronicdevices according to the predicted time parameter and a random factor.For example, even if the predicted time parameter is not determined tobe optimal, a random factor may be introduced for reinforcementlearning. For example, at certain intervals (such as 5% of the time),the processor circuitry may be configured to initiate the establishmentof the network. This may encourage the prediction model to learn moreabout the environment and for example avoid remaining in a locallyoptimal strategy (such as in the farming scenario, the electronicdevices of half of herd of animals learned to establish a network atdawn and the other half learned to establish a network in the evening).

An example scenario may for example be in a farming application, whereone or more animals, wearing one or more electronic devices according tothe present disclosure, may be active during daytime, may be spread outfrom each other, and/or may be far from the farm where the sensor datais to be reported. Then, when dawn or night comes, the animals maygather again in the farm. The predicted time parameter may for examplebe indicative of the prediction of the time slot where the animalsgather in the farm again for dawn or at night (for example for sleepingor eating), and thereby a convenient time slot for initiating anestablishment of a network with the one or more other electronic devicesworn by the other animals, since the one or more electronic devices arecloser to each other than during daytime and thereby a network (such asa local network) may be established between the one or more electronicdevices.

In one or more example electronic devices, the sensor data comprises oneor more of: data sensed by the sensor circuitry, and network qualitydata. The sensor data may comprise time data indicative of the time ofthe day and/or the date of the day.

In one or more example electronic devices, the network quality datacomprises one or more of: a time parameter, a latency parameter, achannel quality parameter, and a retransmission parameter. In one ormore embodiments, the sensor data (such as sensor values) may berecorded with the current time as well as measurement of network qualitydata (such as time to connect, latency, retries). The network qualitydata may for example comprise a time to connect, a latency of thenetwork, a number of retries, a bandwidth of the network, a signalstrength of a received signal, a transmission power of a transmittedsignal, and/or a packet loss rate. For example, if a channel timeslotting (such as channel hopping) applies, the network quality data(such as network conditions) may vary based on which band or timeslot isused.

In one or more embodiments, the network quality data may be used totrain a network quality in the prediction model.

In one or more example electronic devices, the sensor data comprisescurrent sensor data and historical sensor data. Historical sensor datamay for example comprise or be indicative of a time slot and/or ahistorical date where it is efficient to perform sensor data reporting(for example in farming, because all the animals are asleep, in trackingbecause the trackers appear to be semi-static or static). This may forexample be indicated by the lack of activity of the animals or a lack ofmovements of objects in a certain time period. In one or moreembodiments, the historical sensor data may be used to determine apattern in the events that are measured (for example the sleep patternof animals, and/or a tracking pattern).

In one or more example electronic devices, the prediction modelcomprises one or more of: a regression model and a classification model.The prediction model may for example comprise or make use of a logisticregression, a gradient boosting, a neural network, artificialintelligence, deep learning, and/or machine learning. The processorcircuitry 302, and/or elements of the processor circuitry 302, such asthe predictor circuitry 302A, in one or more embodiments, may beconfigured to employ artificial intelligence scheme(s) and/or be trainedusing a supervised machine learning scheme and/or using an unsupervisedmachine learning scheme.

The prediction model may be based on a neural network (such as aconvolutional neural network, a deep learning neural network, and/or acombined learning circuitry). The predictor circuitry 302A may beconfigured to determine (and optionally identify) one or more patternsin existing data (sensor data, and/or predicted time parameter data) inorder to facilitate making predictions for subsequent predicted timeparameter. Additional prediction models may be generated to providesubstantially reliable predictions of time para meters for networkestablishment.

The predictor circuitry 302A may be configured to operate according to asupervised machine learning scheme configured to determine a rule or apattern or a relation that maps inputs to outputs, so that whensubsequent novel inputs are provided the predictor circuitry 302A may,based upon the rule, pattern or relation, accurately predict the correctoutput. In some embodiments, the prediction model may first extract oneor more features from input sensor data, such as by using signalprocessing methods (such as filters), statistics of the signals (such asmean, max, median, and/or quantile), and/or results from unsupervisedlearning methods (such as dimension reduction methods, clustering,and/or auto-encoder). The one or more features may then be fed into aregression and/or classification model that is trained using supervisedmachine learning techniques. Furthermore, a regularization scheme may beapplied to reduce overfitting (for example, limiting the depth of treesin random forest, or some loss term for the size of the weights of aneural network, etc.).

In one or more example electronic devices, the processor circuitry 302is configured to perform a service discovery to find a service in thenetwork. The processor circuitry 302 may be configured to perform aservice discovery to find a service in the network, such as a datastorage service, a firmware update service, a prediction model parameterupdating or sharing service, and/or a task scheduler service.

In one or more example electronic devices, the processor circuitry 302is configured to communicate, for example via the interface 303, with aservice device and to determine with the service device a scheme for howsensor data is to be reported. In other words, the electronic device 300may contact the service device and agree on a scheme for how sensor datamay be reported (such as, sensor data sent), for example based on howmuch sensor data that is to be transferred and depending on therequirements from the other nodes in the network.

In one or more example electronic devices, the processor circuitry 302is configured to enter a power saving mode after reporting sensor data.

In other words, when the electronic device 300 has terminated thereporting of sensor data (such as concluded the task to be done) and isnot relaying sensor data for other electronic devices (such as othernodes), the electronic device may enter a power saving mode (such asswitching off the radio).

In one or more example electronic devices, the processor circuitry 302is configured to determine, based on a previously determined predictedtime parameter, the predicted time parameter. For example, theprediction model that the predictor circuitry operates according to, maybe updated at regular interval based on recent sensor data recorded.

In one or more example electronic devices, the processor circuitry 302is configured to train and/or update the prediction model based on oneor more of: the sensor data, and the predicted time parameter. In one ormore embodiments, the processor circuitry 302 may be configured to trainand/or update the prediction model based on the outcome of the networkestablishment (for example, by comparing the predicted time parameterand the time it actually took to establish the network). The predictionmodel that the predictor circuitry operates according to, may be trainedand/or updated (such as retrained or finetuned). The training of theprediction model may be a supervised learning setup, where the sensordata in the input data and the network quality data can be labelled. Theprediction model or changes to the prediction model may be based on newdata, such as new sensor data, and/or new prediction data.

In one or more example electronic devices, the processor circuitry 302is configured to transmit, for example via the interface 303, theprediction model and/or parameters indicative of the prediction model tothe one or more other electronic devices.

In one or more example electronic devices, the processor circuitry 302is configured to transmit, for example via the interface 303, theupdated and/or trained prediction model (and/or a lossy compressedversion of the prediction model). In other words, the prediction modeland/or parameters indicative of the prediction model (such as a trainedand/or updated prediction model) may be shared and/or updated betweenthe electronic device and the one or more other electronic devices (forexample by using federated learning techniques, such as when the networkis established). This may provide a faster and more robust training,updating, learning and/or adaptation. In one or more embodiments, thetransmission (such as sharing) of the prediction model and/or parametersindicative of the prediction model to the one or more other electronicdevices may for example be performed using broadcast, multicast,anycast, and/or unicast.

The processor circuitry 302 is optionally configured to perform any ofthe operations disclosed in FIGS. 3A-B (such as any one or more of S108,S110, S112, S114, S116, S118, S120). The operations of the electronicdevice 300 may be embodied in the form of executable logic routines(such as lines of code, software programs, etc.) that are stored on anon-transitory computer readable medium (such as on the memory circuitry301) and are executed by the processor circuitry 302).

Furthermore, the operations of the electronic device 300 may beconsidered a method that the electronic device 300 is configured tocarry out. Also, while the described functions and operations may beimplemented in software, such functionality may as well be carried outvia dedicated hardware or firmware, or some combination of hardware,firmware and/or software.

The memory circuitry 301 may be one or more of a buffer, a flash memory,a hard drive, a removable media, a volatile memory, a non-volatilememory, a random access memory (RAM), or other suitable device. In atypical arrangement, the memory circuitry 301 may include a non-volatilememory for long term data storage and a volatile memory that functionsas system memory for the processor circuitry 302. The memory circuitry301 may exchange data with the processor circuitry 302 over a data bus.Control lines and an address bus between the memory circuitry 301 andthe processor circuitry 302 also may be present (not shown in FIG. 2 ).The memory circuitry 301 is considered a non-transitory computerreadable medium.

The memory circuitry 301 may be configured to store the predictionmodel, the sensor data, the one or more predicted time parameter, and/orthe parameters of the prediction model in a part of the memory.

FIGS. 3A and 3B show flow-charts illustrating an example method 100performed by an electronic device (such as the electronic devicedisclosed herein, such electronic device 300 of FIGS. 1 and 2 ), forpredicting initiation of establishment of a network with one or moreother electronic devices (such as the one or more other electronicdevices disclosed herein, such as the one or more other electronicdevices 300A of FIG. 1 and FIG. 2 ).

The method 100 comprises obtaining S102 sensor data.

The method 100 comprises determining S104, based on the sensor data anda prediction model, a predicted time parameter indicative of aprediction of a time slot to initiate an establishment of a network withone or more other electronic devices.

The method 100 comprises initiating S106 the establishment of thenetwork with the one or more other electronic devices according to thepredicted time parameter.

In one or more example methods, the sensor data comprises one or moreof: data sensed by the sensor circuitry, and network quality data.

In one or more example methods, the network quality data comprises oneor more of: a time parameter, a latency parameter, a channel qualityparameter, and a retransmission parameter.

In one or more example methods, the sensor data comprises current sensordata and historical sensor data.

In one or more example methods, the prediction model comprises one ormore of: a regression model and a classification model.

In one or more example methods, the method 100 comprises performing S108a service discovery to find a service in the network.

In one or more example methods, the method 100 comprises communicatingS110 with a service device.

In one or more example methods, the method 100 comprises determiningS112 with the service device a scheme for how sensor data is to bereported.

In one or more example methods, the method 100 comprises entering S114 apower saving mode after reporting sensor data.

In one or more example methods, the method 100 comprises determiningS116, based on a previously determined predicted time parameter, thepredicted time parameter.

In one or more example methods, the method 100 comprises training and/orupdating S118 the prediction model based on one or more of: the sensordata, and the predicted time parameter.

In one or more example methods, the method 100 comprises transmittingS120 the prediction model and/or parameters indicative of the predictionmodel to the one or more other electronic devices.

Embodiments of methods and electronic devices according to thedisclosure are set out in the following items:

-   -   Item 1. An electronic device (300) comprising:        -   a memory circuitry (301);        -   an interface circuitry (303);        -   a sensor circuitry (304) and        -   a processor circuitry (302) comprising a predictor circuitry            (302A) configured to operate according to a prediction            model;        -   the processor circuitry (302) being configured to:            -   obtain sensor data;            -   determine, based on the sensor data, using the predictor                circuitry (302A), a predicted time parameter indicative                of a prediction of a time slot to initiate an                establishment of a network with one or more other                electronic devices; and            -   initiate the establishment of the network with the one                or more other electronic devices according to the                predicted time parameter.    -   Item 2. The electronic device according to item 1, wherein the        sensor data comprises one or more of: data sensed by the sensor        circuitry, and network quality data.    -   Item 3. The electronic device according to item 2, wherein the        network quality data comprises one or more of: a time parameter,        a latency parameter, a channel quality parameter, and a        retransmission parameter.    -   Item 4. The electronic device according to any of items 1-3,        wherein the sensor data comprises current sensor data and        historical sensor data.    -   Item 5. The electronic device according to any of items 1-4,        wherein the prediction model comprises one or more of: a        regression model and a classification model.    -   Item 6. The electronic device according to any of items 1-5,        wherein the processor circuitry (302) is configured to perform a        service discovery to find a service in the network.    -   Item 7. The electronic device according to any of items 1-6,        wherein the processor circuitry (302) is configured to        communicate with a service device and to determine with the        service device a scheme for how sensor data is to be reported.    -   Item 8. The electronic device according to any of items 1-7,        wherein the processor circuitry (302) is configured to enter a        power saving mode after reporting sensor data.    -   Item 9. The electronic device according to any of items 1-8,        wherein the processor circuitry (302) is configured to        determine, based on a previously determined predicted time        parameter, the predicted time parameter.    -   Item 10. The electronic device according to any of items 1-9,        wherein the processor circuitry (302) is configured to train        and/or update the prediction model based on one or more of: the        sensor data, and the predicted time parameter.    -   Item 11. The electronic device according to any of items 1-10,        wherein the processor circuitry (302) is configured to transmit        the prediction model and/or parameters indicative of the        prediction model to the one or more other electronic devices.    -   Item 12. A method, performed by an electronic device, for        predicting initiation of establishment of a network with one or        more other electronic devices, the method comprising:        -   obtaining (S102) sensor data;        -   determining (S104), based on the sensor data and a            prediction model, a predicted time parameter indicative of a            prediction of a time slot to initiate an establishment of a            network with one or more other electronic devices; and        -   initiating (S106) the establishment of the network with the            one or more other electronic devices according to the            predicted time parameter.    -   Item 13. The method according to item 12, wherein the sensor        data comprises one or more of: data sensed by the sensor        circuitry, and network quality data.    -   Item 14. The method according to item 13, wherein the network        quality data comprises one or more of: a time parameter, a        latency parameter, a channel quality parameter, and a        retransmission parameter.    -   Item 15. The method according to any of items 12-14, wherein the        sensor data comprises current sensor data and historical sensor        data.    -   Item 16. The method according to any of items 12-15, wherein the        prediction model comprises one or more of: a regression model        and a classification model.    -   Item 17. The method according to any of items 12-16, the method        comprising:        -   performing (S108) a service discovery to find a service in            the network.    -   Item 18. The method according to any of items 12-17, the method        comprising:        -   communicating (S110) with a service device, and        -   determining (S112) with the service device a scheme for how            sensor data is to be reported.    -   Item 19. The method according to any of items 12-18, the method        comprising:        -   entering (S114) a power saving mode after reporting sensor            data.    -   Item 20. The method according to any of items 12-19, the method        comprising:        -   determining (S116), based on a previously determined            predicted time parameter, the predicted time parameter.    -   Item 21. The method according to any of items 12-20, the method        comprising:        -   training and/or updating (S118) the prediction model based            on one or more of: the sensor data, and the predicted time            parameter.    -   Item 22. The method according to any of items 12-21, the method        comprising:        -   transmitting (S120) the prediction model and/or parameters            indicative of the prediction model to the one or more other            electronic devices.

The use of the terms “first”, “second”, “third” and “fourth”, “primary”,“secondary”, “tertiary” etc. does not imply any particular order, butare included to identify individual elements. Moreover, the use of theterms “first”, “second”, “third” and “fourth”, “primary”, “secondary”,“tertiary” etc. does not denote any order or importance, but rather theterms “first”, “second”, “third” and “fourth”, “primary”, “secondary”,“tertiary” etc. are used to distinguish one element from another. Notethat the words “first”, “second”, “third” and “fourth”, “primary”,“secondary”, “tertiary” etc. are used here and elsewhere for labellingpurposes only and are not intended to denote any specific spatial ortemporal ordering. Furthermore, the labelling of a first element doesnot imply the presence of a second element and vice versa.

It may be appreciated that FIGS. 1-3B comprises some circuitries oroperations which are illustrated with a solid line and some circuitriesor operations which are illustrated with a dashed line. The circuitriesor operations which are comprised in a solid line are circuitries oroperations which are comprised in the broadest example embodiment. Thecircuitries or operations which are comprised in a dashed line areexample embodiments which may be comprised in, or a part of, or arefurther circuitries or operations which may be taken in addition to thecircuitries or operations of the solid line example embodiments. Itshould be appreciated that these operations need not be performed inorder presented. Furthermore, it should be appreciated that not all ofthe operations need to be performed. The example operations may beperformed in any order and in any combination.

It is to be noted that the word “comprising” does not necessarilyexclude the presence of other elements or steps than those listed.

It is to be noted that the words “a” or “an” preceding an element do notexclude the presence of a plurality of such elements.

It should further be noted that any reference signs do not limit thescope of the claims, that the example embodiments may be implemented atleast in part by means of both hardware and software, and that several“means”, “units” or “devices” may be represented by the same item ofhardware.

The various example methods, devices, nodes and systems described hereinare described in the general context of method steps or processes, whichmay be implemented in one aspect by a computer program product, embodiedin a computer-readable medium, including computer-executableinstructions, such as program code, executed by computers in networkedenvironments. A computer-readable medium may include removable andnon-removable storage devices including, but not limited to, Read OnlyMemory (ROM), Random Access Memory (RAM), compact discs (CDs), digitalversatile discs (DVD), etc. Generally, program circuitries may includeroutines, programs, objects, components, data structures, etc. thatperform specified tasks or implement specific abstract data types.Computer-executable instructions, associated data structures, andprogram circuitries represent examples of program code for executingsteps of the methods disclosed herein. The particular sequence of suchexecutable instructions or associated data structures representsexamples of corresponding acts for implementing the functions describedin such steps or processes.

Although features have been shown and described, it will be understoodthat they are not intended to limit the claimed disclosure, and it willbe made obvious to those skilled in the art that various changes andmodifications may be made without departing from the scope of theclaimed disclosure. The specification and drawings are, accordingly, tobe regarded in an illustrative rather than restrictive sense. Theclaimed disclosure is intended to cover all alternatives, modifications,and equivalents.

What is claimed is:
 1. An electronic device comprising: a memorycircuitry; an interface circuitry; a sensor circuitry and a processorcircuitry comprising a predictor circuitry configured to operateaccording to a prediction model; the processor circuitry beingconfigured to: obtain sensor data, the sensor data comprising one ormore of: data sensed by the sensor circuitry, and network quality data;determine, based on the sensor data, using the predictor circuitry, apredicted time parameter indicative of a prediction of a time slot toinitiate an establishment of a network with one or more other electronicdevices; and initiate the establishment of the network with the one ormore other electronic devices according to the predicted time parameter.2. The electronic device according to claim 1, wherein the networkquality data comprises one or more of: a time parameter, a latencyparameter, a channel quality parameter, and a retransmission parameter.3. The electronic device according to claim 1, wherein the sensor datacomprises current sensor data and historical sensor data.
 4. Theelectronic device according to claim 1, wherein the prediction modelcomprises one or more of: a regression model and a classification model.5. The electronic device according to claim 1, wherein the processorcircuitry is configured to perform a service discovery to find a servicein the network.
 6. The electronic device according to claim 1, whereinthe processor circuitry is configured to communicate with a servicedevice and to determine with the service device a scheme for how sensordata is to be reported.
 7. The electronic device according to claim 1,wherein the processor circuitry is configured to enter a power savingmode after reporting sensor data.
 8. The electronic device according toclaim 1, wherein the processor circuitry is configured to determine,based on a previously determined predicted time parameter, the predictedtime parameter.
 9. The electronic device according to claim 1, whereinthe processor circuitry is configured to train and/or update theprediction model based on one or more of: the sensor data, and thepredicted time parameter.
 10. The electronic device according to claim1, wherein the processor circuitry is configured to transmit theprediction model and/or parameters indicative of the prediction model tothe one or more other electronic devices.
 11. A method, performed by anelectronic device comprising a sensor circuitry, for predictinginitiation of establishment of a network with one or more otherelectronic devices, the method comprising: obtaining sensor data, thesensor data comprising one or more of: data sensed by the sensorcircuitry, and network quality data; determining, based on the sensordata and a prediction model, a predicted time parameter indicative of aprediction of a time slot to initiate an establishment of the networkwith the one or more other electronic devices; and initiating theestablishment of the network with the one or more other electronicdevices according to the predicted time parameter.
 12. The methodaccording to claim 11, wherein the network quality data comprises one ormore of: a time parameter, a latency parameter, a channel qualityparameter, and a retransmission parameter.
 13. The method according toclaim 11, wherein the sensor data comprises current sensor data andhistorical sensor data.
 14. The method according to claim 11, whereinthe prediction model comprises one or more of: a regression model and aclassification model.
 15. The method according to claim 11, the methodcomprising: performing a service discovery to find a service in thenetwork.
 16. The method according to claim 11, the method comprising:communicating with a service device, and determining with the servicedevice a scheme for how sensor data is to be reported.
 17. The methodaccording to claim 11, the method comprising: entering a power savingmode after reporting sensor data.
 18. The method according to claim 11,the method comprising: determining, based on a previously determinedpredicted time parameter, the predicted time parameter.
 19. The methodaccording to claim 11, the method comprising: training and/or updatingthe prediction model based on one or more of: the sensor data, and thepredicted time parameter.
 20. The method according to claim 11, themethod comprising: transmitting the prediction model and/or parametersindicative of the prediction model to the one or more other electronicdevices.